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The University of Hong Kong (HKU), Hong Kong
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ScaleLUT is presented, a hardware-oriented LUT design framework and fully parallel reconfigurable accelerator for real-time multi-scale SR deployment and demonstrates the effectiveness of joint LUT algorithm-hardware co-design for practical and energy-efficient edge SR deployment.
Hybrid-LUT slashes LUT storage by two-thirds while enhancing image denoising quality, outperforming existing methods by a significant margin.
Achieving state-of-the-art multimodal performance with only 208.62 million unique images and a theoretical training cost of just $400K challenges the notion that larger datasets and budgets are always necessary for success.
